Epi Midterm Ii

Front 1

Cross-sectional studies
and diseases with long duration

Back 1

Will identify high proportion of prevalent cases with
long duration

People with short disease duration may not be
identified as diseased

If disease duration is associated with exposure,
then results can be biased
*Example: Those with severe emphysema (outcome) are
more likely to smoke (exposure) and have higher case
fatality rate (shorter disease duration) than those with less
severe emphysema.

Front 2

Internal Validity

Back 2

Was the study well done?
Are the findings valid?

Need to consider…
- If there are major methodological problems
- If findings could be due to bias,
confounding, random error

**Important – Need to establish sound internal
validity before you consider generalizing the
results beyond the study population

Front 3

External Validity

Back 3

Aka “generalizability” to target population

To what extent are the participants you have
studied representative of all people with the
outcome of interest?

Need to examine…
- Who did not participate in the study
- Characteristics of study participants that might
preclude you from generalizing the study results
to others who were not in the study

Front 4

Clinical Trial

Back 4

Controlled study that prospectively evaluates
the effect of an allocated exposure (i.e.,
intervention) on the outcome of interest

Effects in which we’re interested: Safety,
efficacy, effectiveness

Considered “gold standard” of epi studies

Front 5

Examples of Exposures and Outcomes in
Epidemiologic Research

Back 5

Exposures:
Medications
• Surgical Procedures
• Behavior Modification
• Screening Programs
• Traits and Behaviors
• Genetic Variants
• Infectious Agents
• Environmental Toxins

Outcomes
• Death
• Disease
• Subclinical Indicators
of Disease
• Health-Related Traits
• Quality of Life
• Physical Function
• Costs

Front 6

Key Parameters of Clinical Trials

Back 6

Individual is unit of observation
Experimental design

Follow participants over time
-Collect data from at least two time
points (e.g., before exposure, after
exposure)

Front 7

When Can a Clinical Trial be Conducted?

Back 7

Clinical trials are justified when uncertainty
exists regarding the effectiveness of a
treatment (aka, EQUIPOISE)

EQUIPOISE: Legitimate uncertainty or
indecision as to choice or course of action…
because of an unknown balance of benefits
and risks

The researcher must believe that…
(1) what a study proposes to accomplish has
an excellent chance of being helpful (i.e.,
will contribute to generalized knowledge)
and
(2) he/she must have justified doubt about
the relative benefits of the comparison
treatment (which may be the “standard of
care” treatment)

Front 8

When Clinical Trials Are Impossible
(or Nearly Impossible)

Back 8

Adverse Exposures (e.g., cigarettes, other
toxins)

Rare Outcomes (e.g., Reye’s Syndrome)

Intervention Already in Wide Use (e.g.,
intensive care unit (ICU) medical care)

Front 9

Basic Protocol in a Clinical Trial

Back 9

1. Obtain approval of Institutional Review
Board (IRB)
2. Enroll participants
3. Gather “baseline” data from participants
4. Allocate exposure to participants
5. Follow-up participants to collect data on
outcome
6. Conduct data analyses
7. Report findings

Front 10

Enrollment of Study Population

Back 10

Enroll a study population with specific
characteristics designed to ensure success of the
trial and safety of participants

Careful application of inclusion/exclusion criteria

Such a study sample will yield the “cleanest”
results (high internal validity), but may
compromise generalizability (low external validity)

Front 11

General Inclusion Criteria

Back 11

• Able to provide informed consent
• At high risk for main outcome
• At low risk for adverse side effects
• No confounding medical conditions
• Likely to adhere to treatment and data
collection/study procedures

Front 12

Non-Randomized Allocation of Exposure

Back 12

Clinical Judgment
e.g., Good surgical candidate vs. Not

Participant Preference
e.g., Wants surgery vs. Doesn’t

Alternating
First Participant --> Treatment; Next --> Control

Day of Week
MWF --> treatment; TuThS --> Control

ID Numbers
Last Digit of SSN Odd --> Treatment; Even --> Control

Front 13

Why Randomize Exposure Allocation?

Back 13

Ensure that exposure assignment is unbiased

Produce similar groups at baseline by known
and unknown factors
Goal: any difference between the groups at
the end of the study will be the result of the
exposure / treatment / intervention

Minimizes the threat of selection bias

Avoids confounding by indication

Front 14

Randomized Assignment

Back 14

Unstratified by any variables
– Assignment is completely random
– Balanced in the long run, but may be
unbalanced in the short run

Stratified by key variables
–Ensures balance within subgroups defined by
key variables before randomization
–Stratification variable should be strongly
related to outcome (e.g., gender, risk level)

Front 15

Confounding by Indication in Observational Studies

Back 15

A bias when patients with the worst prognosis
are allocated preferentially to a particular
treatment.

High risk hypertensive patients are more likely to have
adverse outcomes.

High risk hypertensive patients are more likely to be
prescribed calcium channel blockers (than other drugs
hypertensive drugs).

Observational studies show that calcium channel
blockers are associated with more adverse outcomes

Front 16

Factorial Design

Back 16

Potentially economical way to test two
treatments simultaneously, if their modes of
action are independent

OR

Method to test for treatment synergy
- Is the effect of the combined treatment different
than expected based on the effects of the treatments
alone?

Front 17

“Cross Over"

Back 17

Crossing from one treatment group to the other

Unplanned crossover:
treatment non-adherence
procedures/protocol should be designed to minimize

Planned crossover design:
administration of treatments one after the other in random (or specified) order
treatment may be followed by a “washout” period

Front 18

Planned “Cross Over”

Back 18

Each participant serves as his/her own control
- creates comparability between treatment groups

Feasible only if…
- Outcomes are recurrent, and
- No “carryover” treatment effect after “washout” period

Randomize order of treatments

Front 19

What is a placebo?

Back 19

A placebo is an inactive or inert intervention or
agent that is given as a substitute for the
treatment and where the participant is not
informed he/she is receiving the active or
inactive intervention

Front 20

Why Use a Placebo (aka Mask Participant)?

Back 20

Masking subjects to exposure assignment
minimizes information bias

Equalizes psychological effects of an
“intervention” (aka placebo effect)

Front 21

The Placebo Effect

Back 21

The effect that is produced by a placebo.

The placebo effect is often measured by
comparison of the effect observed in patients
receiving the placebo with the effect observed
in patients receiving the active treatment.

Front 22

Possible Differential Placebo Effects

Back 22

Physical qualities of a pill, e.g. red vs. blue, big
vs. small, branded vs. not

Device/high tech intervention vs. not

Injection vs. oral placebo

Headache relief was 6% higher in groups
that received subcutaneous (i.e., an
injection) placebo versus oral placebo (de
Craen et al, J Neurol 2000)

Front 23

Who to Mask/Blind in the Study and Why

Back 23

Participants: Quantify placebo effects
Physicians: Uniform care apart from study
Data Collectors: Uniform outcome ascertainment
Data Analysts: Reduce threat of analytic bias

Front 24

Partial Masking

Back 24

In some circumstances masking of participants
and/or physicians may be impossible or unethical
(Surgery, behavior modification)

In this setting, others can generally still be
masked:
Data collectors
Adjudicators
Laboratory measurements
Data analysts

Front 25

Ascertainment of Outcomes

Back 25

Devise clear, a priori outcome definition(s)

Prefer “harder” to “softer” outcomes

Don’t forget about adverse effects/events

Mask to the fullest extent possible

Standardize methods and equipment

Train and certify data collectors

Conduct on-going quality assurance

Front 26

Non-compliance

Back 26

Study Non-compliance:
o Persons who stop participating in study
o Do not adhere to protocol

Treatment Non-compliance: poop
o Persons who do not take all of assigned
treatment (i.e., poor adherence)

Front 27

Approaches to Non-compliance

Back 27

Run-in period / pilot study – randomize subjects
after a trial period assessing compliance

Monitor noncompliance:
- Interview patients, count pills
- Medication bottle devices
- Blood or urine tests
- Directly observed treatment

In the setting of non-compliance, the observed
effect will likely be smaller than the true effect

Front 28

CT Data Analysis Approach

Back 28

1. Intention to Treat (ITT)

2. Treatment received
o Observational
o No longer have benefits of randomization

3. Subgroup analyses
o Small numbers
o Hard to determine that treatment effect differs
by sub-groups

Front 29

Intention to Treat (ITT) Approach

Back 29

Analysis by assigned treatment regardless of
the observed course of treatment

Maintains initial balance from randomization

Highlights problems from adverse effects

Conservative approach

Strongly recommended as primary approach

Front 30

Number needed to treat (NNT)

Back 30

Number of patients who would need to be treated
to prevent one outcome

NNT = 1 / (outcome frequency in untreated group
– outcome frequency in treated group)

Small NNT is good

Estimates often presented with 95% confidence
intervals

Front 31

Number needed to harm (NNH)

Back 31

Number of patients who would need to be treated
to cause one patient to be harmed (by treatmentrelated adverse events or side effects)

NNH = 1 / (adverse event frequency in treated
group – adverse event frequency in untreated
group)

Large NNH is good

Estimates often presented with 95% confidence
intervals

Front 32

Safety and Stopping

Back 32

“Stopping rule”

A rule set before the start of the trial that
specifies a limit for the observed treatment
difference for the primary outcome which, if
exceeded, automatically leads to the termination
of the treatment or control arm (depending on
direction of the difference)

Front 33

When to stop a clinical trial before
its scheduled end?

Back 33

1. Clear evidence of benefit
2. Clear evidence of harm

--> Importance of plans to monitor the
progress of a trial

Front 34

Monitoring Progress (Benefits/Harms)
in a Clinical Trial

Back 34

Data Safety and Monitoring Board (DSMB):

Independent committee with responsibilities
to periodically review accumulated data for
evidence of benefit or harm (e.g., adverse
events) from the treatment

Responsible for making recommendations
for modifying the trial, including stopping
early, if appropriate

Front 35

Why CTs Can Be Difficult

Back 35

Hard to find and recruit the right people

Great responsibility on the investigator(s), need
for tremendous documentation, cost

May take years for outcomes to develop

People are free to do as they please:
- Some assigned to treatment don’t adhere
- Some assigned to control seek treatment
- Some drop out of the trial completely (loss-tofollow-up)

Front 36

Advantages of CTs

Back 36

"Gold standard” (Randomization) of epi studies

Designed to minimize bias

“Highest quality evidence available”

Results may be combined into systematic
reviews

Front 37

NNT and herd immunity

Back 37

Number needed to vaccinate (NNV)

If vaccine coverage rates are high, vaccination
will produce positive herd-immunity effects.
Hence, if high coverage rates are attained, the
population-based NNV will likely be lower.

On the other hand, if coverage rates are low,
accounting for herd-immunity effects will have
little or no effect on estimates of NNV

Front 38

Limitations of CTs

Back 38

Cost

Limited external validity
- Country, patient characteristics, study
procedures, outcome measures

Time to conduct and to publish findings

Difficult to study rare events

Difficult to study distant events

Narrowing of the studied question

Front 39

Phases in Clinical Trials

Back 39

I Evaluate safety, dosage-->10-20 healthy volunteers -->Unexpected side effects may occur

II Evaluate efficacy --> About 200 patients-->Most drugs fail in Phase II due to being less efficacious than anticipated

III Evaluate effectiveness--> More than 1,000 patients-->Likelihood to detect rare side effects increases with number of patients

IV Evaluate long-term safety and effectiveness--> 1,000s of patients, “real life” evaluation outside of research environment-->Previously untested groups may show
adverse reactions, postmarketing surveillance

Front 40

Definition of Cohort Study

Back 40

Observational epidemiologic study that follows
groups with common characteristics over time

Terms associated with cohort studies: followup, incidence, longitudinal study

Participants defined by exposure status, then
followed for outcomes of interest

Front 41

Key Parameters of Cohort Studies

Back 41

Individual is unit of observation

Observational design

Follow participants over time
-Collect data from at least two time points

Participants selected based on exposure
status, and all are “at risk” for the main
outcome at baseline

Front 42

When is a Cohort Study Warranted?

Back 42

Good evidence of an association of the disease
with a certain exposure

Exposure is rare, but incidence of disease
among exposed is high

Time between exposure and disease is short

Attrition of study population can be minimized

Front 43

Types of Populations in Cohort Studies

Back 43

Open or dynamic:
Changeable characteristic (e.g.,smoking)
Members come and go; losses may occur
Incidence rate

Fixed:
Irrevocable event (e.g., birth of child)
Does not gain members; losses may occur
Incidence Rate

Closed:
Irrevocable event (e.g.,natural disaster)
Does not gain members; no losses occur (observation period is short)
Cumulative incidence

Front 44

Timing of Cohort Studies

Back 44

Prospective – Looking forward in time
Participants grouped based on past or current
exposures and followed forward for outcome

Retrospective – Looking back in time
Both exposures and outcomes have already
occurred when study begins, data collection is
based on existing records (historical)

Ambidirectional – Looking both forward and
back in time

Front 45

Basic Protocol in a Prospective Cohort Study

Back 45

1. Obtain approval of Institutional Review
Board (IRB)
2. Enroll participants grouped by exposure
status
3. Gather “baseline” data from participants
4. Follow participants to collect data on
outcomes
5. Conduct data analyses
6. Report findings

Front 46

Selection of Exposed Group (Cohort)

Back 46

Depends on hypothesis, exposure frequency,
feasibility considerations

Special cohorts for rare exposures
-Uncommon workplace exposures, unusual diets,
uncommon lifestyles
-Occupational groups, religious groups

General cohorts for common exposures
-Professional groups or well defined geographic areas
Facilitate follow-up and accurate ascertainment of
outcomes

Front 47

Selection of Unexposed Group (Cohort)

Back 47

Internal comparison group
-Unexposed members of same cohort, ideal
option

General population
-Second best option, based on preexisting
population data on disease morbidity and
mortality

Comparison cohort
-Members of another cohort, least desirable
option, results can be difficult to interpret

Front 48

Characterizing the Exposure (Cohort)

Back 48

Exposed or index group vs. Unexposed or
referent or comparison group

Must specify minimum amount of exposure to
qualify as “exposed”

May divide exposed group into levels
-Example: High, medium, low
-Detection of dose response relationship

Front 49

Things Happen Over Time
Affecting Exposures and Outcomes (Cohort)

Back 49

Aging

Environmental exposures
-Lifestyle
-External environments

Disease identification

Front 50

Cohort Sources of Information

Back 50

Interviews

Medical and employment records

Direct physical exams

Lab tests and biological specimens

Environmental monitoring

And remember…
Each source has advantages and disadvantages
Need comparable procedures for data collection in
exposed and unexposed groups, including standard
outcome definitions and masking

Front 51

Follow-up and Outcome Assessment (Cohort)

Back 51

Exposed and unexposed groups monitored
for outcome(s) over time during follow-up

More than one outcome is usually studied

Follow-up can range from a few hours to a
several decades

Difficult to maintain contact with participants

Aim to have a high “follow-up rate” by
minimizing losses to follow-up

Front 52

Maximizing Participant Retention (Cohort)

Back 52

Losses to follow-up (LTF) decrease sample size
LTF may be more like to develop outcome!

Collection of data at baseline on participant,
friends, relatives, physicians

Regular contact via mail, phone, home visits

If possible LTF – Then, “Address Correction
Requested,” contacts provided at baseline,
directories, national registries, commercial
companies

Front 53

Cohort Study Data Analysis Approach

Back 53

Primary objective – Compare disease
occurrence in exposed and unexposed groups
-->Incidence rates, cumulative incidence

Person-time

Induction period – Interval between action of a
cause (e.g., exposure) and disease onset

Latent period – Interval between disease onset
and clinical diagnosis

Front 54

Disadvantages of Cohort Studies

Back 54

Inefficient for rare outcomes

Poor info on exposures and other key variables
(retrospective)

Expensive and time consuming (particularly
prospective)

Inefficient for diseases with long induction and
latent periods (prospective)

More vulnerable to bias (retrospective)

Front 55

Advantages of Cohort Studies

Back 55

Efficient for rare exposures

Good information on exposures (prospective)

Can evaluate multiple effects of an exposure

Efficient for diseases with long induction and
latent periods (retrospective)

Less vulnerable to bias (prospective)

Can directly measure disease incidence or risk

Clear temporal relationship between exposure
and outcome (prospective)

Front 56

Information Bias in Cohort Studies

Back 56

Information bias is a flaw in collecting or
measuring exposure or outcome data that
results in different quality/accuracy of
information between comparison groups

In retrospective cohort studies, you rely on
past records

What if past records differed in quality and
extent of info between exposed and
unexposed persons?

Front 57

Definition of a Case-Control Study

Back 57

Observational epidemiologic study of persons
with the outcome of interest (“cases”) and
without (“controls”) that examines the presence
of particular attributes (“exposures”) in the two
groups

Participants defined by outcome status, then
exposures of interest are assessed

Highly efficient study design

Front 58

Key Parameters of Case-Control Studies

Back 58

Individual is unit of observation

Observational design

No follow-up of participants over time
(i.e., the investigator does not directly
collect data from the participant over time)
- Collect data at one time point

Participants selected based on outcome
status, then exposures are assessed

Front 59

Types of Case-Control Studies

Back 59

Population-based:
-Participants identified from within a source population
-No pre-existing study infrastructure
-Example: Inpatients at Johns Hopkins Hospital today

Nested:
-Source population is ongoing cohort study
-Benefits of cohort and case-control study designs
-Example: Participants in ALIVE cohort study

Front 60

Basic Protocol in a
Case-Control Study

Back 60

1. Obtain approval of Institutional Review
Board (IRB)
2. Identify and enroll cases and controls
3. Gather data from participants, records, etc
4. Conduct data analyses
5. Report findings

Front 61

Selection of Cases (case control) : Case definition

Back 61

Goal – accurate classification
Signs and symptoms
Physical and pathological exams
Results of diagnostic testing
Criteria used have implications for accuracy
of case definition
Best to use all available evidence
Disagreement about how to define disease,
disease definitions change over time

Front 62

Selection of Cases: Sources of Case Identification(case control)

Back 62

Hospital/clinic patient rosters
Death certificates
Special surveys (e.g., NHANES)
Special reporting systems (e.g., birth and/or death
registries)
Each source as advantages/disadvantages
Goal is to identify as many true cases as
possible (and as cheaply and quickly as
possible)

Front 63

Selection of Cases
Incident versus prevalent cases(case control)

Back 63

Incidence, if studying causes of disease
Prevalence, if study duration of disease
Might not have a choice, so prevalence

Front 64

Selection of Cases: Complete versus partial case ascertainment(case control)

Back 64

As long as source population can be defined, then…
- Do not have to include all cases in a population
-A subset of cases is appropriate

Front 65

Selection of Controls
Individuals without the disease(case control)

Back 65

A sample of the population that produced the
cases
AKA, “referent group” because it “refers to”
exposure distribution in the source population
Cases and controls represent the same base
population…
So, a member of the control group who gets the
disease would end up being a case in the study
Selected independent of exposure status!

Front 66

Assumption (case control)

Back 66

Cases and Controls Originate
From Same Hypothetical Source Population

Front 67

Selection of Controls(case control)
Sources of Control Identification

Back 67

Population (preferred, if available)
Hospital/clinic
Deceased individuals
Case’s friends, spouse, and relatives
Each source has advantages/disadvantages
Remember - If a member of the control group
actually had the disease, would he/she end up
as a case in the study?

Front 68

Ratio of Controls to Cases

Back 68

Can increase the statistical power of the study
to detect an association by increasing the size
of the control group

Up to a ratio of 4 controls : 1 case will increase
power

Beyond 4:1, not considered worthwhile due to
costs

Front 69

Methods of Sampling Controls
in a Nested Case-Control Study

Back 69

Each time a case occurs, a control is selected
from the “risk-set” of individuals remaining in the
cohort without the condition

Cumulative incidence sampling
-Risk-set is defined at the end of follow-up
among those without the outcome

Incidence density sampling
-Risk-set is defined at the time of the case
-Controls can be future cases!

Front 70

Challenge in Case-Control Studies

Back 70

Cases and controls may differ in characteristics or
exposures other than the one targeted for study
-Is study finding due to exposure, or due to differences
between cases and controls?

Solution via study design: Match cases and control
for factors about which you’re concerned

Matching: Process of selecting the controls so they
are similar to the cases in certain characteristics
(e.g., age, race, sex, etc)

Front 71

Matching (Case Control)

Back 71

Group matching (frequency matching)
Proportion of controls with a certain characteristic
is identical to the proportion of cases with the
same characteristic
# of controls may be less than # of cases

Individual matching (matched pairs)
For each case, at least one control is selected
who is similar to the case for the characteristic of
interest

Front 72

Problems with Matching (Case Control)

Back 72

Practical –A lot of matching may make it
impossible to find a suitable control

Conceptual – Once you match controls to cases
by a certain characteristic, then you cannot
study that characteristic in your analysis

So, only match on factors you are convinced are
risk factors for the disease (and you therefore
don’t need to investigate)

Front 73

Sources of Exposure Information(Case Control)

Back 73

Questionnaires: Face-to-face, telephone, self administered/ Can obtain info on may exposures; must be carefully designed and administered to elicit accurate info; expensive

Preexisting records: Administrative, medical, regulatory/ May be only available source of exposure info; avoids bias;
may be incomplete; may lack uniformity and details; inexpensive

Biomarkers: Levels in blood, urine, bone, toenails/ Estimate of internal dose; infrequently used because of
difficultly identifying valid and reliable markers of exposure to noninfectious agents; expensive

Front 74

Limitations in Recall(Case Control)

Back 74

Virtually all people are limited to varying
degrees in their ability to recall information

If this limitation affects all subjects in a study to
the same extent, misclassification may result

Generally leads to underestimation of the
association between exposure and outcome

Front 75

(Case Control)Recall Bias

Back 75

Differential recall of past exposures/events
between cases and controls
-Different by amount of recall
-Different by accuracy of recall

Can affect study findings on exposure and
outcome association

Front 76

Case-Control Study
Data Analysis Approach

Back 76

Challenge: Often, investigators do not know
the size of the total population that produced
the cases

Assumption: Cases and Controls Originate
From Same Hypothetical Source Population

So, we don’t know how many people were “at
risk” for becoming a case (i.e., we don’t know
the denominator), so we can’t calculate
incidence, prevalence, associated measures

But, we can calculate the odds!

Odds of event = probability (p) the event will
occur divided by the probability the event will
not occur = p / (1-p)

Front 77

Disadvantages of Case-Control Studies

Back 77

Inefficient for rare exposures

May have poor info on exposures because of
retrospective

Vulnerable to bias because of retrospective

Cannot establish temporal relationship between
exposure and disease

Front 78

Advantages of Case-Control Studies

Back 78

Efficiency
-Less time, less money than cohort studies,
experimental studies

Efficient for rare diseases

Efficient for disease with long induction and
latent periods

Can evaluate multiple exposures in relation to
outcome (so, good for diseases about which
little is known)

Front 79

Measuring the Risk of Disease

Back 79

Absolute risk = Incidence of disease
- No explicit comparison by exposure status

Measures of excess risk – Involves comparison
by exposure status
– Ratio measures (relative risk, prevalence
ratio, odds ratio)
– Difference measures (incidence difference,
prevalence difference)

Front 80

What is a measure of association?

Back 80

A quantity that expresses the strength or degree
of association (i.e. relationship) between
variables.

Association: Statistical dependence between
two or more events, characteristics, or other
variables.

In epi studies, the association in which we are
interested is between EXPOSURE and
OUTCOME

IMPORTANT:
An association may be fortuitous, spurious, or may
be produced by various other circumstances
The presence of an association does not
necessarily imply a causal relationship

Front 81

When interpreting a measure of association… We need to evaluate:

Back 81

1. Reference group - The choice of reference group
(‘referent’)
2. Direction - The direction of the estimated measure of
association provides information on the nature of the
influence of the exposure on the outcome
3. Magnitude - The magnitude of the estimated measure
of association provides information about the strength
of the relationship between the exposure and outcome

Front 82

Approaches to Measuring Excess Risk
(i.e., Excess Incidence) in Measures of Assoc

Back 82

1. Ratio of Risks:
Risk in Exposed/ Risk in Non-Exposed

2. Differences in Risk:
(Risk in Exposed) - (Risk in Non-Exposed)

Sound familiar from outbreak
investigation calculations?

Front 83

Choice of the Reference Group

Back 83

When comparing measures of disease frequency,
which group is the referent?

Depends on the research question!
-The choice may be arbitrary
-Often: Choose group with largest sample size
-Simple binary exposure: Reference = ‘Unexposed’

When reporting ratio measures of association in
tabular format, you typically see the reference group
noted by ‘1.0’ or ‘Reference’ or ‘REF’ in a table

Front 84

Continuous Exposure: What is Reference?

Back 84

Continuous exposure: Measured on interval scale
e.g., Diastolic Blood Pressure, CD4+ cell
counts, Age, Smoking Pack-years

What is the reference group?
-None!
-Measures of association reflect increased
disease frequency per unit increase in
exposure
-Need to be clear about units

Front 85

Case-Control Study
Data Analysis Approach

Back 85

Since, we don’t know how many people were
“at risk” for becoming a case (i.e., we don’t
know the denominator), we can’t calculate
incidence, prevalence, associated measures,
like relative risk and prevalence ratio

But, we can calculate the odds!

Odds of event = probability (p) the event will
occur divided by the probability the event will
not occur = p / (1-p)

Front 86

When is the OR a good estimate of the RR?

Back 86

When the cases are representative of all people
with the disease in the population from which the
cases were drawn, with regard to history of the
exposure.

When the controls are representative of all
people without the disease in the population from
which the cases were drawn, with regard to
history of exposure.

When the disease is not frequent (i.e., rare).

Front 87

Attributable Risk For the Total Population

Back 87

If the incidence in the total population is
unknown, it can be calculated if we know:
1. The incidence among exposed.
2. The incidence among unexposed.
3. The proportion of the total population that is
exposed.

Front 88

Reviewing Public Health Surveillance

Back 88

Passive – Routine reporting of disease cases
seen in health care facilities

Active - Special search to find disease cases

Sentinel – Disease-specific reporting systems
in defined catchment areas

Syndromic – Uses already existing healthrelated data that precede diagnosis, supplements existing surveillance methods

Front 89

Basic Concepts of Disease Causation

Back 89

Disease causation is multi-factorial

Pathogenesis is generally a multi-step
sequence that may or may not result in
clinical disease

Agent-host-environment model

Front 90

Epidemiologic Triangle

Back 90

Agents:
Biologic (e.g., bacteria, virus)
Chemical (e.g., poison, smoke)
Physical (e.g., injuries)
Nutritional (lack and/or excess)

Host (factors):
Age
Sex
Genetics
Personality (e.g., risk taking)
Health status (i.e., previous disease)
Immune status
Nutritional status
Education

Environment:
(physical)
Temperature
Crowding
Housing
Neighborhood
Water
Sewage
Food
Radiation
Air pollution
(Social)
Social support
Behavioral modeling (e.g., family,
peers, culture, media)
Politics
Economics

Front 91

“Risk Factor”

Back 91

An attribute associated with the occurrence of
health-related condition

- Aspect of personal behavior or lifestyle
- Environmental exposure
- Inborn or inherited characteristics
- Not necessarily causal

Several nuanced synonyms: predisposing
factor, risk marker, precursor, determinant

Front 92

Three Essential Characteristics of a Cause

Back 92

Association: a cause must occur together
with its effect; a statistical association must
exist between a cause and its putative effect.

Time order: a cause must precede its effect.

Direction: there is an asymmetrical
relationship between the cause and effect;
a change in the cause produces a change
in the effect; changing the effect does not
alter the cause.

Front 93

Necessary Versus Sufficient Causes

Back 93

Necessary cause = a cause that must
always precede an effect (A Necessary Cause
must always precede the effect)

Sufficient cause = a cause that always
produces an effect

Note: The effect need not be the sole result
of one cause.

Front 94

Necessary Causes

Back 94

If a disease is defined by the presence of an
agent, that agent is necessary by definition.

Example: Tuberculosis can only be caused by
the tubercle bacillus.

Contrast: Hepatitis can be caused by many
viruses, but Hepatitis C is caused only by the
Hepatitis C virus.

Front 95

Any Given Cause May Be Necessary,
Sufficient, Both, or Neither

Back 95

Necessary and sufficient = cause is always
present with disease; nothing but cause is
needed to result in disease
–Example: measles virus and measles

Necessary and not sufficient = cause is
always present with disease, but disease is
not always present with cause
–Example: HPV and cervical cancer

Not necessary and sufficient = cause may or
may not be present with disease, nothing but
cause is needed to result in disease
–Example: High-dose exposure to pesticides
or ionizing radiation and sterility in men

Not necessary and not sufficient = cause may or
may not be present with disease; if cause is
present with disease, then some additional
factor must also be present
–Example: sedentary lifestyle and coronary
heart disease

Front 96

Necessary Conditions

Back 96

“X is a necessary condition for Y” =
If we don't have X, then we won't have Y
OR
Without X, you won't have Y
To say that X is a necessary condition for Y
does not mean that X guarantees Y.

Front 97

Sufficient Conditions

Back 97

“X is a sufficient condition for Y” =
if we have X, we know that Y must follow
OR
X guarantees Y

Front 98

Epidemiologic Guidelines for Establishing
a Cause-Effect Relationship

Back 98

Temporal sequence
Strength of the association
Dose response relationship / biologic gradient
Consistency of the association / replication
Coherence (biologic plausibility)
Specificity of the association
Experiment (cessation of exposure)
Analogy
Consideration of alternate explanations

Front 99

Temporal Sequence

Back 99

Study designs that can establish the potential
“cause” (risk factor or treatment) precedes the
disease include:
-Clinical trial
-Cohort study

Study designs that cannot establish that the
potential “cause” preceded the disease include:
-Cross-sectional study
-Population-based case-control study
-Ecologic study

Front 100

Strength of the Association

Back 100

Measures of association include:
-Ratio measures
Examples: relative risk, odds ratio

Difference measures
Examples: risk difference, rate
difference, attributable risk

The stronger the association between the
potential risk factor and the disease, the less
likely is the association due to confounding.

But, it is possible on occasion for strong
associations to be produced by confounding,
biases, or chance variability

Front 101

Probabilistic Causality

Back 101

The strength of a causal relationship is
assessed by the magnitude of its measures
of association.

The greater the RR or OR, the closer the
cause is to being necessary and/or
sufficient.

Front 102

Strength of the Association
Dose-Response Relationship

Back 102

Does risk for disease increase
with the degree of exposure?
Exception: “threshold effect”

Front 103

Consistency of the Association

Back 103

“Has it been repeatedly observed by different
persons, in different places, circumstances,
and times?”

Consistency is persuasive only if the studies
use different architectures, methodologies, and
subject groups and still come up with the same
results.

Example: Heart disease and high blood
pressure

Front 104

Coherence of the Association
(Biologic Plausibility)

Back 104

“The whole thing should make biologic and
epidemiologic sense.”

Caution: This criterion is used to test
whether evidence in favor of a hypothesis is
plausible based on currently existing theory
and knowledge

Front 105

Specificity of the Association

Back 105

“The precision with which the occurrence of
one variable, to the exclusion of others, will
predict the occurrence of another, again to the
exclusion of others.”

But, the closer one gets to specificity, the
easier it is to detect the association of the
cause and the disease.

The extreme case would be a necessary
and sufficient cause for a disease.

Front 106

Specificity of the Association:
NOT a valid criterion!

Back 106

1. “causes” have multiple effects
2. Diseases have multiple causes

Front 107

When assessing evidence, ask if the
observed association could be…

Back 107

1. Due to chance?
2. Due to bias?
3. Due to confounding?

Front 108

Confidence Interval (CI)

Back 108

A computed interval that, upon repeated
sampling, has a given probability (e.g., 95%)
of containing the true value of a statistical
parameter (e.g., ratio, proportion, rate).



In other words…

For a 95% confidence interval, if a single
population is repeatedly sampled, then 95%
of the samples would capture the true value
of the population parameter.

Expresses the precision of the point estimate
- More narrow interval = more precision
- Less narrow interval = less precision
Calculated with predetermined significance
level, α (alpha), which is often set at 0.05

Front 109

Two Principal Types of Bias

Back 109

1. Selection Bias
2. Information Bias

Front 110

Selection Bias

Back 110

Error due to systematic differences in
characteristics between those who take part
in a study and those who do not

The problem is that the association between
exposure and outcome may differ between
those who participate in the study and those
who do no

The measure of association is distorted due
to procedures used to select subjects and
from factors that influence study
participation

Usually inferred, rather than observed

Front 111

Selection Bias Types

Back 111

Self-selection bias
• Selection of controls
– Healthy worker effect
• Post-entry exclusion bias

Front 112

Information Bias

Back 112

A flaw in collecting or measuring exposure
or outcome data that results in different
quality/accuracy of information between
comparison groups

Can result in distortion of the measure of
association

Front 113

Information Bias Types

Back 113

Misclassification
- Differential and non-differential with
respect to exposure and outcome
status
• Recall bias
• Reporting bias
• Interviewer bias
• Surveillance bias / biased follow-up

Front 114

Bias can distort the measure of association

Back 114

Away from the null (e.g., away from 1.0):
i.e., observe stronger than true association between
exposure and outcome
True RR = 1.5
Observed RR = 3.2

Toward the null (e.g., toward 1.0):
i.e., observe weaker than true association between
exposure and outcome
True OR = 2.6
Observed OR = 1.2

Front 115

Distinguishing between random error (i.e.,
chance) and systematic error (i.e., bias)

Back 115

Imagine that a given study could be
increased in size until it was infinitely large

Some errors would be reduced to zero; these
are the random errors

Other errors would not affected by increasing
the size of the study; these are systematic
errors or bias

Front 116

Confounding

Back 116

A situation in which the measure of association
is distorted because of the relationship
between the exposure and a third factor that
also influences the outcome.

It is a true phenomenon, and not an error in
the study.

Distortion in a measure of association due to a
third variable that:
1. Is associated with the exposure
2. Influences the outcome
3. Is not in the causal pathway (i.e., not an
intermediate step between exposure and
outcome)

Front 117

Consequences of Failing to
Account for Confounding

Back 117

In this example, ignoring the confounder (i.e.,
smoking) would have resulted in a “false
positive” finding of the association between
heavy alcohol consumption and MI.

The opposite can also be true, ignoring a
confounder can obscure a true association
between an exposure and an outcome,
leading us to conclude that there is no
association when an association truly exists.

Front 118

Confounding can be controlled for…

Back 118

1. In the study design
• Matching in a case-control study
• Randomization in a clinical trial
2. In the data analysis
• Stratification
• “Adjustment” (e.g., age adjustment)
• Multivariate regression models
BUT, we must have collected the data!

Front 119

Interaction (i.e., Effect Modification)

Back 119

If the size of the association between an
exposure and an outcome is changed or
modified by the level of a third variable,
interaction is said to be present
Interaction is also called “effect measure
modification”
Classic examples: age, immunization

Front 120

Assessing Effect Modification

Back 120

The presence or absence of an interaction
depends upon the measure of effect that is
being used, e.g., risk difference, relative risk

Note: If you detect effect modification, then
report it via stratum-specific estimates. Do
not “adjust” for it in your data analyses.

Front 121

Model for Additive Effect

Back 121

Combined Total Risk of A and B =
Baseline Risk
+ Attributable Risk (A)
+ Attributable Risk (B)

Combined Effect of A and B =
Attributable Risk (A)
+ Attributable Risk (B)

Front 122

Model for Multiplicative Effect

Back 122

)
Combined Total Risk of A and B =
Baseline Risk
x Relative Risk (A)
x Relative Risk (B)

Combined Effect of A and B =
Relative Risk (A) x Relative Risk (B)

Front 123

Possible Types of Effect Modification

Back 123

Antagonism: Combined effect less than
predicted by the model (negative interaction)

Synergism: Combined effect greater than
predicted by the model (positive interaction)

Front 124

Comparison of
Confounding and Effect Modification

Back 124

Confounding – Association between exposure and
outcome is distorted by a third variable related to
the exposure and outcome

Effect modification – The association between
exposure and outcome is modified by levels of a
third variable

Front 125

Distinguishing between
confounding and effect modification

Back 125

1. Make list of potential confounders and effect
modifiers (literature review, data collected)
2. Calculate “crude” measure of association for
exposure and outcome of interest
3. Stratify association by levels of potential
confounder or potential effect modifier
4. Compare crude vs. stratum-specific
associations…

If stratified associations are relatively similar
across strata AND different from crude, then you
have confounding

If stratified associations differ across strata AND
crude association seems to be weighed-average of
stratum-specific associations (i.e., crude measure
is between stratum-specific measures), then you
have effect modification